Crowd assisted query system

ABSTRACT

Aspects of the present disclosure relate to a network-based crowd assisted query system that includes a client device in communication with an application server executing the crowd assisted query system over a network. For example, the crowd assisted query system may be or include a group of one or more server machines. Users of the crowd assisted query system are presented with a graphical user interface (GUI) configured to receive queries that include data objects, wherein the data objects include representations of unidentified items of interest to the user. The data objects may include media content, such as graphical images as well as audio data, and in some example embodiments may further include text data describing the unidentified items.

TECHNICAL FIELD

The subject matter of the present disclosure generally relates to userinterfaces for client devices. In particular, example embodiments relateto a user interface to receive and display data objects to facilitatecrowd assisted search queries.

BACKGROUND

The amount of electronically stored data in networked systems andmarketplaces has grown tremendously. Much of the growth of theelectronically stored data is a direct result of the sheer number ofindividuals that create and post item listings representative of itemsfor sale. As anyone who has ever attempted to search these networkedsystems knows, the electronically stored data is practically uselessunless it can be conveniently and accurately searched. Due to the vastamount of electronic data, and the often inconsistent naming conventionsof item listings, finding specific items through standard search methodshas become difficult if not impossible.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and are not intended to limit itsscope to the illustrated embodiments. On the contrary, these examplesare intended to cover alternatives, modifications, and equivalents asmay be included within the scope of the disclosure.

FIG. 1 is a network diagram illustrating a networked system having aclient-server architecture configured for exchanging data over a networkwith a crowd assisted query system, according to example embodiments.

FIG. 2 is a block diagram illustrating various functional components ofa crowd assisted query system, which is provided as part of thenetworked system, according to example embodiments.

FIG. 3 is a flow chart illustrating a method for populating a trainingdatabase to train an artificial intelligence module, according to anexample embodiment.

FIG. 4 is a flow chart illustrating a method for ranking a descriptionsuggestion provided by a user by the crowd assisted query system,according to an example embodiment.

FIG. 5 is a flow chart illustrating a method for receiving a rewardvalue associated with the query request, wherein the reward value isoffered in exchange for identification of the unidentified item of thequery request by a description suggestion, according to an exampleembodiment.

FIG. 6 is a diagram depicting an event flow of a requesting user and asuggesting user, according to an example embodiment.

FIG. 7 is an interface diagram illustrating a user interface forreceiving a query request, according to an example embodiment.

FIG. 8 is an interface diagram illustrating a query request interface toreceive query requests, according to an example embodiment.

FIG. 9 is an interface diagram illustrating a suggestion interface toreceive query suggestions, according to an example embodiment.

FIG. 10 is an interface diagram illustrating the suggestion interfaceconfigured to receive a query suggestion, according to an exampleembodiment.

FIG. 11 is a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions for causingthe machine to perform any one or more of the methodologies discussedherein may be executed.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter of the present disclosure. Inthe following description, specific details are set forth in order toprovide a thorough understanding of the subject matter. It shall beappreciated that embodiments may be practiced without some or all ofthese specific details.

Aspects of the present disclosure relate to a network-based crowdassisted query system that includes a client device in communicationwith an application server executing the crowd assisted query systemover a network. For example, the crowd assisted query system may be orinclude a group of one or more server machines. Users of the crowdassisted query system are presented with a graphical user interface(GUI) configured to receive queries that include data objects, whereinthe data objects include representations of unidentified items ofinterest to the user. The data objects may include media content, suchas graphical images as well as audio data, and in some exampleembodiments may further include text data describing the unidentifieditems.

The crowd assisted query system is configured to generate and causedisplay of at least two different GUIs at client devices: a first GUI toreceive query requests from client devices; and a second GUI to receivedescription suggestions on the queries from client devices. For example,the first GUI to receive query requests from client devices may includeone or more fields in which a user may provide (e.g., upload) dataobjects such as media content to be appended to the query, such asgraphical images, photos, audio content, and the like, as well as textdata to further describe aspects of the media content. The media contentmay represent an item of interest to the user which the user is unableto identify. For example, the media content uploaded by the user mayinclude a photograph of a chair, and/or a text string that states“leather lounge chair.” In some example embodiments, the GUI may furtherinclude user selectable options to categorize the data object of thequery, such as “furniture,” or “jewelry,” or “clothing.” A single dataobject may have one or more associated data category.

The second GUI may include a presentation of representations of dataobjects uploaded by users in queries, organized by category, andconfigured to include fields to receive description suggestions fromusers familiar with the unidentified items represented by the dataobjects. For example, a description suggestion may include a string oftext that describes the unidentified object represented by the dataobject (e.g., “Eames Chair”), as well as a reference to an item thatmeets the description of the data object (e.g., a link to an itemlisting for a similar item). A suggesting user may browse through therepresentations of data objects displayed within the second GUI andprovide description suggestions through user inputs into the second GUI.

The crowd assisted query system receives queries that include a dataobject from a requesting user via the first GUI from a first clientdevice. The data object includes media content depicting an unidentifieditem of interest to the requesting user. Upon receipt of the query, thecrowd assisted query system parses the data objects from the query, andassigns the data object to an item category based on attributes of thedata object. For example, the attributes may include features of thedata object itself (e.g., image data of an image), as well as itemcategory selections made by the requesting user (e.g., “clothing,”“furniture,” etc.).

Upon receiving and categorizing a data object from a query, the crowdassisted query system may receive a request to display queries within asecond GUI in order to receive description suggestions from suggestingusers. For example, a suggesting user may provide a user input into thesecond GUI requesting that the crowd assisted query system cause displayof representations of data objects from queries in specified categories(e.g., “clothes,” “furniture”), wherein the representations includefields to provide description suggestions. The suggesting user maybrowse through the data objects and provide description suggestions thatinclude text strings as well as references to existing item listings.

The crowd assisted query system may cause display of a notification tothe client device of the requesting user, and cause display of apresentation of a set of description suggestions received for the query.The requesting user may thereby review the available descriptionsuggestions and make a selection of the “best” description that matchesthe unidentified desired item. The requesting user may view thedescription suggestions provided by suggesting users and select thedescription suggestion that most closely matches the unidentified itemdepicted in the image. For example, the requesting user may explicitlyselect a description suggestion through a user input, or in someembodiments, the crowd assisted query system may detect a search requestor purchase transaction conducted by the requesting user that includesthe description suggestion.

In some example embodiments, the queries received from the requestingusers include reward values assigned by the requesting user, wherein thereward values are offered to suggesting users in exchange fordescription suggestions. The requesting user may thereby review thedescription suggestions provided for a query and select the “best”match. In response to receiving the selection, the reward value isawarded to the corresponding suggesting user by the crowd assisted querysystem.

In response to receiving the selection of the description selection bythe requesting user, the crowd assisted query system ranks thedescription suggestions associated with the data object. For example,the description suggestions selected by the requested user may be rankedmore highly than description suggestions not selected by the requestinguser. In some example embodiments, the requesting user may also providea user input indicating that a particular description suggestion doesnot match the query, and should be removed as an option.

In further example embodiments, the description selections may befurther ranked based on attributes of the suggesting users. Userattributes may include: frequency with which a suggestion user submitsdescription suggestions; a rate at which description suggestions from asuggesting user are selected; as well as an area of expertise of asuggesting user (e.g., “clothing,” furniture,” “technology”). Forexample, description suggestions from suggesting users that submitdescription suggestions that are selected frequently may be ranked morehighly than a description suggestion from a first time suggester.

As an illustrative example from a user perspective, consider thefollowing example. A user sees an item they like, for example a chair,but they have no way of determining what to search for within anetworked marketplace in order to locate the item. The user takes aphoto of the chair with a mobile device, and uploads the image of thechair into a GUI of the crowd assisted search system. In the process ofuploading the photo of the chair, the user may additionally provide userinputs to indicate an item category for the chair (e.g., furniture), aswell as descriptive information to further indicate what they aresearching for (e.g., “leather 60s modern lounge chair”). The userattaches a “bounty” (e.g., reward value) to the photo of the chair,wherein the bounty is offered in exchange for a group of suggestingusers to provide description suggestions identifying what exactly theuser should be looking for. For example, if a description suggestion isselected by the requesting user, the suggesting user is awarded thebounty.

Having uploaded the photo of the chair into the GUI, the crowd assistedquery system causes display of a representation of the chair within afeed populated with other similar items (e.g., based on item category).Suggesting users may browse through the feed and provide descriptionsuggestions on items which they are able to identify. For example, asuggesting user may come upon the photo of the chair uploaded by therequesting user, and based on the photo and the descriptive information,may know what to search for. The suggesting user may provide adescription suggestion that comprises a text string or reference to anitem or item listing that matches the chair in the photo. The suggestinguser may provide a text string that indicates “Eames Chair,” or auniform resource locator (URL) that leads to an item listing for amatching item. In this way, suggesting users provide descriptionsuggestions to the representation of the photo of the chair receivedfrom the requesting user.

The crowd assisted query system notifies the requesting user that one ormore description suggestions have been received from suggesting users.The requesting user reviews the available description suggestions inorder to select the “best” description available that matches the itemthey were searching for most closely. The user may provide inputsindicating when a description suggestion is wrong, or when thedescription suggestion is correct. The crowd assisted query system mayconduct a search of a networked marketplace or search engine in responseto the user selecting one or more of the description suggestions. Havingdetermined that the description suggestion for “Eames Chair” is a matchto the item the user was searching for, the crowd assisted query systemawards the bounty to the corresponding suggesting user.

Having determined that the description suggestion indicating that thephoto of the chair is an “Eames Chair,” the crowd assisted query systemassociated the description suggestion with the photo of the chair withinan artificial intelligence (AI) database. In this way, the crowdassisted query system populates the AI database with descriptionsuggestions and data objects (e.g., photos and media items of requestsfrom requesting users). Ultimately, with enough entries (e.g., requestsand suggestions), the crowd assisted query system may train an imagesearch AI that would then be able to identify items based on a visualsearch alone, such that a user could provide an image of an item and thesystem would be able to identify what the item is based on the imagealone, with much greater specificity than just “chair,” or “shirt.”

FIG. 1 is a network diagram depicting a networked system 100 having aclient-server architecture configured for exchanging data over a network102 with an application server 104, according to example embodiments.While the networked system 100 is depicted as having a client-serverarchitecture, the present inventive subject matter is, of course, notlimited to such an architecture, and could equally well find applicationin an event-driven, distributed, or peer-to-peer architecture system,for example. Further, to avoid obscuring the inventive subject matterwith unnecessary detail, various functional components that are notgermane to conveying an understanding of the inventive subject matterhave been omitted from FIG. 1. Moreover, it shall be appreciated thatalthough the various functional components of the networked system 100are discussed in a singular sense, multiple instances of any one of thevarious functional components may be employed.

As shown, the networked system 100 includes the application server 104in communication with client device(s) 106 and a third party server 105over the network 102. The application server 104 communicates andexchanges data with entities within the networked system 100 thatpertain to various functions and aspects associated with the networkedsystem 100 and its users. These data exchanges may include transmitting,receiving (communicating), and processing data to, from, and regardingcontent and users of the networked system 100.

The application server 104 includes a crowd assisted query system 110 toprovide server-side functionality, via the network 102 (e.g., theInternet), to client devices such as the client device 106, and theclient device 108.

The client devices 106 and 108 may be any sort of mobile device (e.g.,smart phone, tablet computer, or wearable device) that includesinput/output components (e.g., touch screen) for displaying content andreceiving user inputs, and a crowd assisted query application 112specifically designed for interacting with the application server 104.For example, a user 107 or 109 may use the crowd assisted queryapplication 112 executing on the client devices 106 and 108 to uploadqueries or suggestions to the queries through a GUI generated by theapplication server 104. The crowd assisted query application 112 may, insome embodiments, when executed by the client device 106, configure theclient device 106 to perform any of the methodologies described herein.

Turning specifically to the application server 104, the applicationserver 104 includes the crowd assisted query system 110 and a database120. In some embodiments, the application server 104 may include anApplication Programming Interface (API) server and/or a web servercoupled to (e.g., via wired or wireless interfaces) the crowd assistedquery system 110 to provide programmatic and/or web interfacesrespectively to the client devices 106 and 108. The application server104 may, in some embodiments, also include a database server coupled tothe crowd assisted query system 110 to facilitate access to the database120. The database 120 may include multiple databases that may beinternal or external to the application server 104.

The crowd assisted query system 110 hosts one or more applications suchas a server-side navigation menu application that provides navigationservices to users (e.g., user 107) who access the application server104. For example, a user 107 may use the crowd assisted queryapplication 112 executing on the client device 106 to display aninterface to upload a query request that includes a set of data objects(e.g., media content, reward value, etc.). The user 107 may for exampleupload images, description information, and the like through theinterface at the client device 106. A user 109 may use the crowdassisted query application 112 executing on the client device 108 toprovide description suggestions through an interface configured todisplay query requests received from users. For example, the interfacemay include a presentation of one or more query requests in a browseablefeed.

The database 120 stores data pertaining to various functions and aspectsassociated with the networked system 100 and its users. For example, thedatabase 120 may include a database that stores item listings andinventory information of a networked marketplace, as well as an AIdatabase that includes training data to train an AI module of the crowdassisted query system 110. The database 120 may also include databasesto maintain user account records for users of the application server104. Each user account record is a data structure that includesinformation that describes aspects of a particular user.

FIG. 1 also illustrates a third party application 124 executing on thethird party server 105 that may offer information or services to thecrowd assisted query system 110 or to users of the client device 106.For example, the third party application 124 may be associated with anyorganization that conducts transactions with or provides services tousers of the client device 106, such as a network-based marketplace. Insome embodiments, the incentives provided to users may be redeemed orotherwise used with the third party application 124.

FIG. 2 is a block diagram illustrating components of the crowd assistedquery system 110 that configure the crowd assisted query system 110 toprovide a query request interface at the client device 106 to receivequery requests that include data objects, generate and cause display ofa feed of query requests within a suggestion interface at the clientdevice 108, receive description suggestions, associate the descriptionsuggestions with a data object within the database 120, and train an AImodule to perform an image based search, according to some exampleembodiments. The crowd assisted query system 110 is shown as including acommunication module 205, a categorization module 210, a presentationmodule 215, an artificial intelligence (AI) module 220, and a rankingmodule 225, all configured to communicate with each other (e.g., via abus, shared memory, or a switch). Any one or more of these modules maybe implemented using one or more processors 230 (e.g., by configuringsuch one or more processors to perform functions described for thatmodule) and hence may include one or more of the processors 230.

Any one or more of the modules described may be implemented usingdedicated hardware alone (e.g., one or more of the processors 230 of amachine) or a combination of hardware and software. For example, anymodule described of the crowd assisted query system 110 may physicallyinclude an arrangement of one or more of the processors 230 (e.g., asubset of or among the one or more processors of the machine) configuredto perform the operations described herein for that module. As anotherexample, any module of the crowd assisted query system 110 may includesoftware, hardware, or both, that configure an arrangement of one ormore processors 230 (e.g., among the one or more processors of themachine) to perform the operations described herein for that module.Accordingly, different modules of the crowd assisted query system 110may include and configure different arrangements of such processors 230or a single arrangement of such processors 230 at different points intime. Moreover, any two or more modules of the crowd assisted querysystem 110 may be combined into a single module, and the functionsdescribed herein for a single module may be subdivided among multiplemodules. Furthermore, according to various example embodiments, modulesdescribed herein as being implemented within a single machine, database,or device may be distributed across multiple machines, databases, ordevices.

FIG. 3 is a flow chart illustrating a method 300 for populating atraining database to train an AI module, according to an exampleembodiment. The method 300 may be embodied in computer-readableinstructions for execution by one or more processors (e.g., processors230 of FIG. 2) such that the steps of the method 300 may be performed inpart or in whole by functional components (e.g., modules) of the clientdevice 106 or the crowd assisted query system 110; accordingly, themethod 300 is described below by way of example with reference thereto.However, it shall be appreciated that the method 300 may be deployed onvarious other hardware configurations and is not intended to be limitedto the functional components of the client device 106 or the crowdassisted query system 110.

At operation 305, the communication module 205 receives a query requestfrom the client device 106. The query request may include one or moredata objects representative of an item of interest to the requestinguser. The item of interest may be an unidentified item that the userwishes to search for, but does not know how. For example, the item ofinterest may be a chair that the user 107 sees and takes a picture of.The crowd assisted query system 110 causes display of a query requestinterface at the client device 106, wherein the query request interfaceincludes a field to upload data objects representative of the item. Forexample, the data objects may include images and text strings, as wellas a selection of an item category. In some example embodiments, theuser 107 may additionally provide a reward value to be assigned to thequery request, offered in exchange for the identification of the itemdepicted in the query request by a suggesting user.

At operation 310, the categorization module 210 assigns the queryrequest to an item category based on the data objects of the queryrequest. For example, the categorization module 210 may assign the queryrequest based on the image data alone (e.g., in instances where the user107 has not provided an item category), based on image and objectrecognition techniques. For example, the AI module 220 may apply aconvolutional neural network in order to identify an item category thatbest matches the item depicted in the image provided by the user 107. Insome embodiments, the categorization module 210 may simply categorizethe query request based on the category selected by the user 107, or thetext description provided by the user 107.

At operation 315, the presentation module 215 causes display of asuggestion interface at the client device 108. The user 109 may forexample be a suggester that wishes to provide description suggestionsfor one or more query requests from requesting users. The user 109 mayselect a query request category, and in response the presentation module215 causes display of a feed that includes representations of one ormore query requests assigned to the query request category selected. Therepresentations may include an image associated with each query request,the corresponding reward value, and any text information that therequesting user provided in the query request. The user 109 may scrollthrough the feed and select one or more query requests that he wishes toprovide description suggestions for.

At operation 320, the communication module 205 receives a descriptionsuggestion from the user 109. For example, the user 109 may select aquery request from among a set of query requests presented in thesuggestion interface at the client device 108. In response to selectingthe query request from among the set of query requests, the presentationmodule 215 causes display of one or more fields for the user 109 toprovide a description suggestion that identifies the item depicted inthe query requests. The description suggestion may include a textstring, as well as a reference to an item at the third party server 105(e.g., a URL).

At operation 325, the categorization module 210 associates thedescription suggestion provided by the user 109 with the data object(s)of the query request within a database (e.g., the database 120). Forexample, the database 120 may include an AI database useable to trainthe AI module 220 to accurately identify items based on text or audioinputs (e.g., natural language speech), or based on image data alone. Atoperation 330, the crowd assisted query system 110 trains the AI module220 based on the database 120. For example, the crowd assisted querysystem 110 may use the database 120 to determine correlations betweenthe description suggestions and the image data. The correlations maythereby be applied to train the AI module 220.

FIG. 4 is a flow chart illustrating a method 400 for ranking adescription suggestion provided by a user 109 by the crowd assistedquery system 110, according to an example embodiment. The method 400 maybe embodied in computer-readable instructions for execution by one ormore processors (e.g., processors 230) such that the steps of the method400 may be performed in part or in whole by functional components (e.g.,modules) of the client device 106 or the crowd assisted query system110; accordingly, the method 400 is described below by way of examplewith reference thereto. However, it shall be appreciated that the method400 may be deployed on various other hardware configurations and is notintended to be limited to the functional components of the client device106 or the crowd assisted query system 110.

One or more operations 405, 410, and 415 of the method 400 may beperformed as part (e.g., a precursor task, a subroutine, or portion) ofthe method 300, in which the crowd assisted query system 110 trains theAI module 220, according to some example embodiments.

At operation 405, in response to receiving the description suggestionfrom the user 109 at operation 320, as discussed in FIG. 3, thecommunication module 205 accesses a user profile associated with theuser 109 to retrieve user profile data. The user profile data mayinclude a list of query request categories that the user 109 hasprovided description suggestions to, a number of description suggestionsfrom the user 109 that have been selected by requesters, and an area ofexpertise of the user 109.

At operation 410, the ranking module 225 calculates a reliability scoreof the description suggestion provided by the user 109 on the queryrequest based on the user profile data. The ranking module 225 maycalculate the reliability score based on the number of descriptionsuggestions from the user 109, the number of description suggestionsfrom the user 109 that have been selected by requesters, and the area ofexpertise of the user 109. For example, the user 109 may have an area ofexpertise, wherein the area of expertise indicates an item category thatthe user 109 has experience or knowledge about. The area of expertisemay be determined based on explicit selection or indication by the user109, or based on user feedback (e.g., users may promote the user 109 ina certain item category).

Having calculated a reliability score of the description suggestion, atoperation 415 the ranking module 225 ranks the description suggestionagainst one or more description suggestions associated with the queryrequest. For example, multiple suggesting users may provide descriptionsuggestions for any one query request. By ranking the descriptionsuggestions based on a reliability score calculated by the rankingmodule 225, the crowd assisted query system 110 may surface and morereadily identify accurate description suggestions. In some exampleembodiments, the presentation module 215 causes display of thedescription suggestions to the user 107 in an order based on the rankingby the ranking module 225.

FIG. 5 is a flow chart illustrating a method 500 for receiving a rewardvalue associated with the query request, wherein the reward value isoffered in exchange for identification of the unidentified item of thequery request by a description suggestion, according to an exampleembodiment. The method 500 may be embodied in computer-readableinstructions for execution by one or more processors (e.g., processors230) such that the steps of the method 500 may be performed in part orin whole by functional components (e.g., modules) of the client device106 or the crowd assisted query system 110; accordingly, the method 500is described below by way of example with reference thereto. However, itshall be appreciated that the method 500 may be deployed on variousother hardware configurations and is not intended to be limited to thefunctional components of the client device 106 or the crowd assistedquery system 110.

One or more operations 505, 510, and 515 of the method 500 may beperformed as part (e.g., a precursor task, a subroutine, or portion) ofthe method 300, in which the crowd assisted query system 110 generatesand causes display of a navigation menu at a client device 106,according to some example embodiments.

At operation 505, the communication module 205 receives a reward valuewith the query request from the client device 106. The query request mayinclude one or more data objects that include a reward value specifiedby the user 107. The reward value is an amount offered by the user 107in exchange for identification of an item (or service) associated withthe query request. For example, the user 107 may offer $1 for any userthat can identify or otherwise provide a description suggestion thatmatches the item represented in the query request from the user 107.

At operation 510, the user 107 selects a description suggestion fromamong one or more description suggestions received from users includingthe user 109. For example, suggesting users may view the query requestfrom the user 107 within a suggestion interface displayed by the crowdassisted query system 110 in order to provide description suggestions.The crowd assisted query system 110 notifies the requesting user (user107) of the description suggestions received, and causes display of anotification at the client device 106 that may include a display of thedescription suggestions. The user 107 may thereby review the descriptionsuggestions received and provide a selection of the “best” descriptionsuggestion that matches the query request.

In some example embodiments, the selection from the user 107 may bebased on an explicit selection of a description suggestion through aninterface provided by the presentation module 215. For example, thepresentation module 215 may cause display of an interface at the clientdevice 106 that includes a set of one or more description suggestions ofwhich the user 107 may make a selection. In further embodiments, theselection may be based on search requests or transactions by the user107. For example, the user 107 may conduct a search request based on adescription suggestion from among the set of description suggestions, orpurchase an item identified by a description suggestion from among thedescription suggestions (e.g., includes a title or description thatincludes the description suggestion).

At operation 515, in response to receiving the selection of thedescription suggestion from the user 107, the crowd assisted querysystem 110 awards the reward value to the appropriate user based on thedescription suggestion selected. For example, the crowd assisted querysystem 110 may award the reward value to the user that provided theselected description suggestion. The crowd assisted query system 110may, for example, access a financial account associated with therequesting user and retrieve the reward value to deliver to the userthat provided the selected description suggestion.

FIG. 6 is a diagram depicting an event flow 600 of a requesting user(e.g., user 107) and a suggesting user (e.g., user 109), according to anexample embodiment. The event flow 600 may be executed by the users 107and 109 via client devices 106 and 108.

A requesting user (e.g., user 107) submits a query request to the crowdassisted query system 110. To submit the query request, at operation605, the user 107 takes (or otherwise selects) a photo of an item whichthe user 107 then uploads to the crowd assisted query system 110 (e.g.,via the client device 106). In some embodiments, the user 107 may uploada photo, and/or a media item that depicts or describes the item. Forexample, in lieu of a photo (or as a supplement), the user 107 mayprovide audio or text data describing an item (e.g., text or audio thatstates “a red patio chair with three legs”).

Having uploaded the photo with the query request, at operation 615 theuser 107 edits the request details. The request details include itemcategory information, and text data that includes a description of theitem. For example, the user 107 may select an item category for thequery request (e.g., “furniture”).

At operation 620, a suggesting user (e.g., user 109) launches the crowdassisted query application 112 on the client device 108. The suggestinguser selects a category at operation 625, and at operation 630, thecrowd assisted query system 110 causes display of a feed of queryrequests associated with the item category selected by the user 109. Insome embodiments, the suggesting user may select multiple itemcategories to view in a feed simultaneously.

At operation 635, the suggesting user selects a query request, such asthe query request submitted by the requesting user (e.g., user 107). Inresponse to receiving the selection, the crowd assisted query system 110causes display of an item detail page that includes a presentation ofthe photo uploaded by the requesting user, as well as additional itemdetails such as description information. At operation 640, thesuggesting user provides an input to initiate a suggestion to the queryrequest. At operation 645, the suggesting user inputs and uploads asuggestion associated with the query request selected. The suggestionmay include a text string as well as a reference to an item located at athird-party server 105. At operation 650, the suggesting user 109finishes the suggesting process. In some embodiments, at operation 655the suggesting user may view one or more suggestions from othersuggesting users associated with the selected query request. In responseto finishing the suggesting process, the crowd assisted query system 110notifies the requesting user of the suggestions via a notificationpresented at the client device 106.

At operation 660, the requesting user may view all suggestionsassociated with the query request uploaded. The suggestions may bedisplayed to the requesting user in a feed such that the requesting usermay “like” or “unlike” suggestions. For example, at operation 665, therequesting user may “unlike” a suggestion, which results in thesuggestion being removed from the set, or as in operation 670, may“like” the suggestion, which may cause the suggestion to be linked tothe photo of the query request.

FIG. 7 is an interface diagram illustrating a user interface 700 forreceiving a query request, according to an example embodiment. The userinterface 700 may be presented on a touch-enabled mobile device such asthe client device 106. The user interface 700 is shown to include asearch request field 705, automatic query suggestions 710, and agraphical icon 715 to receive a query request from the user 107.

A user 107 may provide a search request into the search request field705 of the user interface 700. In response, the crowd assisted querysystem 110 may cause display of a set of query suggestions 710 based onthe search request. The set of query suggestions 710 may be based on atransaction history of the user 107 and the search request entered inthe search request field 705. The user 107 may instead choose toinitiate a query request via an input to the graphical icon 715.

FIG. 8 is an interface diagram illustrating a query request interface800 (e.g., displayed at a client device 106) to receive query requests,according to an example embodiment. The query request interface 800 isshown to include a display of an image 805 uploaded by a requesting user(e.g., user 107), a reward entry field 810 to receive a reward value toassign to the query request, as well as a category selection menu 815 toreceive a selection of an item category associated with the queryrequest.

In some example embodiments, the query request interface 800 isdisplayed at the client device 106 in response to receiving a user inputto initiate a query request, for example via the graphical icon 715 ofFIG. 7. The query request interface 800 is configured to receive queryrequests. The query requests include data objects such as the image 805,an object category, text data, as well as a reward value. For example,having selected an image to upload, the user 107 may provide additionaltext data describing the image (or specific features of the image). Theuser 107 provides a reward value into the reward entry field 810, andmay specify an item category via the category selection menu 815. Forexample, in response to receiving a selection of the category selectionmenu 815, the crowd assisted query system 110 may cause display of a setof object categories from which the user 107 may select one or more.

FIG. 9 is an interface diagram illustrating a suggestion interface 900(e.g., displayed at a client device 108) configured to receive querysuggestions, according to an example embodiment. As shown in FIG. 9, thesuggestion interface 900 is shown to include a set of graphical elementsrepresentative of item categories 905. A suggesting user (e.g., user109) may select an item category from among the set of item categories905, and in response, the crowd assisted query system 110 causes displayof a corresponding set of query requests assigned to the selected itemcategory. The suggesting user may thereby view the set of query requeststo provide query suggestions in exchange for the corresponding rewardvalues.

FIG. 10 is an interface diagram illustrating the suggestion interface900 configured to receive a query suggestion, according to an exampleembodiment. The suggestion interface 900 is shown to include a depictionof the image 805 of a query request submitted by a requesting user(e.g., the user 107). A suggesting user (e.g., user 109) may provide aquery suggestion through the suggestion interface 900 by providing thetext data 1005. In some example embodiments, the user 109 may provide areference to an item located at the third-party server 105. Havingprovided the text data 1005, the user 109 may provide an input into thesuggestion icon 1010, and in response, the crowd assisted query system110 may index the query suggestion at a memory location associated withthe query request, and the image 805, within the database 120.

In some example embodiments, in response to receiving the input into thesuggestion icon 1010, the crowd assisted query system 110 causes displayof a notification at the client device 106, to notify the requestinguser (e.g., user 107) of the query suggestion. The requesting user maythereby view query suggestions in order to “like,” “unlike,” or removethe query suggestions from the database 120.

Machine Architecture

FIG. 11 is a block diagram illustrating components of a machine 1100,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 11 shows a diagrammatic representation of the machine1100 in the example form of a computer system, within which instructions1116 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1100 to perform any oneor more of the methodologies discussed herein may be executed. Theseinstructions transform the general, non-programmed machine into aspecially configured machine programmed to carry out the described andillustrated functions described herein. Consistent with someembodiments, the machine 1100 may correspond to the client device 106 orthe crowd assisted query system 110.

The machine 1100 may operate as a standalone device or may be coupled(e.g., networked) to other machines. In a networked deployment, themachine 1100 may operate in the capacity of a server machine or a clientmachine in a server-client network environment, or as a peer machine ina peer-to-peer (or distributed) network environment. By way ofnon-limiting example, the machine 1100 may comprise or correspond to aserver computer, a client computer, a personal computer (PC), a tabletcomputer, a laptop computer, a netbook, a set-top box (STB), a personaldigital assistant (PDA), an entertainment media system, a cellulartelephone, a smart phone, a mobile device, a wearable device (e.g., asmart watch), a smart home device (e.g., a smart appliance), other smartdevices, a web appliance, a network router, a network switch, a networkbridge, or any machine capable of executing the instructions 1116,sequentially or otherwise, that specify actions to be taken by machine1100. Further, while only a single machine 1100 is illustrated, the term“machine” shall also be taken to include a collection of machines 1100that individually or jointly execute the instructions 1116 to performany one or more of the methodologies discussed herein.

The machine 1100 may include processors 1110, memory 1130, and input andoutput (I/O) components 1150, which may be configured to communicatewith each other such as via a bus 1102. In an example embodiment, theprocessors 1110 (e.g., a Central Processing Unit (CPU), a ReducedInstruction Set Computing (RISC) processor, a Complex Instruction SetComputing (CISC) processor, a Graphics Processing Unit (GPU), a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, processor1112 and processor 1114 that may execute instructions 1116. The term“processor” is intended to include multi-core processor that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.11 shows multiple processors 1110, the machine 1100 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core process), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory/storage 1130 may include a memory 1132, such as a mainmemory, or other memory storage, and a storage unit 1136, bothaccessible to the processors 1110 such as via the bus 1102. The storageunit 1136 and memory 1132 store the instructions 1116 embodying any oneor more of the methodologies or functions described herein. Theinstructions 1116 may also reside, completely or partially, within thememory 1132, within the storage unit 1136, within at least one of theprocessors 1110 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine1100. Accordingly, the memory 1132, the storage unit 1136, and thememory of processors 1110 are examples of machine-readable media.

As used herein, “machine-readable medium” means a device able to storeinstructions and data temporarily or permanently and may include, but isnot be limited to, random-access memory (RAM), read-only memory (ROM),buffer memory, flash memory, optical media, magnetic media, cachememory, other types of storage (e.g., Erasable Programmable Read-OnlyMemory (EEPROM)) and/or any suitable combination thereof. The term“machine-readable medium” should be taken to include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store instructions 1016. The term“machine-readable medium” shall also be taken to include any medium, orcombination of multiple media, that is capable of storing instructions(e.g., instructions 1116) for execution by a machine (e.g., machine1100), such that the instructions, when executed by one or moreprocessors of the machine 1100 (e.g., processors 1110), cause themachine 1100 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

The I/O components 1150 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1150 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components1150 may include many other components that are not shown in FIG. 11.The I/O components 1150 are grouped according to functionality merelyfor simplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1150 mayinclude output components 1152 and input components 1154. The outputcomponents 1152 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1154 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1150 may includebiometric components 1156, motion components 1158, environmentalcomponents 1160, or position components 1162, among a wide array ofother components. For example, the biometric components 1156 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1158 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1160 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 1162 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1150 may include communication components 1164operable to couple the machine 1100 to a network 1180 or devices 1170via coupling 1182 and coupling 1172, respectively. For example, thecommunication components 1164 may include a network interface componentor other suitable device to interface with the network 1180. In furtherexamples, communication components 1164 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1170 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a UniversalSerial Bus (USB)).

Moreover, the communication components 1164 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1164 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1164, such as location via Internet Protocol (IP) geo-location, locationvia Wi-Fi® signal triangulation, location via detecting a NFC beaconsignal that may indicate a particular location, and so forth.

Transmission Medium

In various example embodiments, one or more portions of the network 1180may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, the network 1180 or a portion of the network 1180may include a wireless or cellular network and the coupling 1182 may bea Code Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or other type of cellular orwireless coupling. In this example, the coupling 1182 may implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard setting organizations, other long rangeprotocols, or other data transfer technology.

The instructions 1116 may be transmitted or received over the network1180 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1164) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1116 may be transmitted or received using a transmission medium via thecoupling 1172 (e.g., a peer-to-peer coupling) to devices 1170. The term“transmission medium” shall be taken to include any intangible mediumthat is capable of storing, encoding, or carrying instructions 1116 forexecution by the machine 1100, and includes digital or analogcommunications signals or other intangible medium to facilitatecommunication of such software.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium or ina transmission signal) or hardware modules. A hardware module is atangible unit capable of performing certain operations and may beconfigured or arranged in a certain manner. In example embodiments, oneor more computer systems (e.g., a standalone, client, or server computersystem) or one or more hardware modules of a computer system (e.g., aprocessor or a group of processors) may be configured by software (e.g.,an application or application portion) as a hardware module thatoperates to perform certain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field-programmable gatearray (FPGA) or an ASIC) to perform certain operations. A hardwaremodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor or other programmableprocessor) that is temporarily configured by software to perform certainoperations. It will be appreciated that the decision to implement ahardware module mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarilyconfigured (e.g., programmed) to operate in a certain manner and/or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses that connect the hardware modules). In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or more processors orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors, not onlyresiding within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment, or a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., APIs).

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, or software, or in combinations ofthem. Example embodiments may be implemented using a computer programproduct, for example, a computer program tangibly embodied in aninformation carrier, for example, in a machine-readable medium forexecution by, or to control the operation of, data processing apparatus,for example, a programmable processor, a computer, or multiplecomputers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a standalone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site, or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry(e.g., an FPGA or an ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that both hardware and software architectures meritconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or in acombination of permanently and temporarily configured hardware may be adesign choice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Although the embodiments of the present invention have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show by way of illustration, and not oflimitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

All publications, patents, and patent documents referred to in thisdocument are incorporated by reference herein in their entirety, asthough individually incorporated by reference. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated referencesshould be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim are still deemed to fall within thescope of that claim.

What is claimed is:
 1. A system comprising: one or more processors; anda non-transitory memory storing instructions that configure the one ormore processors to perform operations comprising: receiving, from afirst client device, a query that includes a request for anidentification of an unidentified item, the request including a dataobject that comprises a text string; assigning the data object to anitem category within a data repository based on the text string of thedata object; receiving, from a second client device, an identificationof the item category; causing display of a representation of the requestwithin a graphical user interface (GUI) at the second client deviceresponsive to the identification of the item category, the GUIcomprising a display of a set of graphical elements representative ofrequests associated with the item category within the data repository;receiving, from the second client device, an input that selects therepresentation of the request, the input including a descriptionsuggestion to be associated with the data object within the datarepository; and training an artificial intelligence (AI) module toidentify the unidentified item based on the description suggestionassociated with the data object within data repository.
 2. The system ofclaim 1, wherein the data object further comprises image data.
 3. Thesystem of claim 1, wherein the receiving the query includes: receivingthe query as a submission through the GUI, wherein the submissionincludes the data attributes of the data object, and wherein the dataattributes include an indication of the item category of theunidentified item.
 4. The system of claim 1, wherein the descriptionsuggestion is a first description suggestion, the machine learningdatabase includes a set of description suggestions associated with thedata object, and the instructions cause the system to perform operationsfurther comprising: ranking the first description suggestion among theset of description suggestions associated with the data object based onthe data attributes; and training the AI module based on the datarepository and the ranking.
 5. The system of claim 4, wherein thereceiving the description suggestion to be applied to the data objectincludes: retrieving user profile data associated with a user of thesecond client device; determining a reliability score of the descriptionsuggestion based on the user profile data of the user; and wherein theranking the first description suggestion is based on the reliabilityscore.
 6. The system of claim 4, wherein the instructions cause thesystem to perform operations further comprising: receiving a selectionof the first description suggestion from the first client device; andwherein the ranking the first description suggestion is based on theselection from the first client device.
 7. The system of claim 1,wherein the receiving the data object from the first client deviceincludes: receiving a reward value from the first client device, thereward value offered for an identification of the unidentified item ofthe data object based on the description suggestion; and wherein theinstructions cause the system to perform operations further comprising:receiving a selection of the description suggestion from the firstclient device; identifying the unidentified item based on the selectionof the description suggestion; and awarding the reward value to thesecond client device in response to the identifying the unidentifieditem.
 8. The system of claim 7, wherein the receiving the selection ofthe description suggestion includes: receiving a search request from thefirst client device, wherein the search requests includes thedescription suggestion.
 9. The system of claim 1, wherein the dataobject is a first data object and the instructions cause the system toperform operations further comprising: receiving a general query fromthe first client device, the query including a second data object;determining a specific query based on the general query, the second dataobject, and the A.I. module; and retrieving a set of search resultsbased on the specific query.
 10. The system of claim 1, wherein the itemcategory comprises a set of data objects, wherein the set of dataobjects have at least some of the data attributes of the data object.11. A method comprising: receiving, from a first client device, a querythat includes a request for an identification of an unidentified item,the request including a data object that comprises a text string;assigning the data object to an item category within a data repositorybased on the text string of the data object; receiving, from a secondclient device, an identification of the item category; causing displayof a representation of the request within a graphical user interface(GUI) at the second client device responsive to the identification ofthe item category, the GUI comprising a display of a set of graphicalelements representative of requests associated with the item categorywithin the data repository; receiving, from the second client device, aninput that selects the representation of the request, the inputincluding a description suggestion to be associated with the data objectwithin the data repository; and training an artificial intelligence (AI)module to identify the unidentified item based on the descriptionsuggestion associated with the data object within the data repository.12. The method of claim 11, wherein the data object further comprisesimage data.
 13. The method of claim 11, wherein the receiving the queryincludes: receiving the query as a submission through the GUI, whereinthe submission includes the data attributes of the data object, andwherein the data attributes include an indication of the item categoryof the unidentified item.
 14. The method of claim 11, wherein thedescription suggestion is a first description suggestion, the machinelearning database includes a set of description suggestions associatedwith the data object, and the method further comprises: ranking thefirst description suggestion among the set of description suggestionsassociated with the data object based on the data attributes; andtraining the AI module based on the data repository and the ranking. 15.The method of claim 14, wherein the receiving the description suggestionto be applied to the data object includes: retrieving user profile dataassociated with a user of the second client device; determining areliability score of the description suggestion based on the userprofile data of the user; and wherein the ranking the first descriptionsuggestion is based on the reliability score.
 16. The method of claim14, wherein the method further comprises: receiving a selection of thedescription suggestion from the first client device; and wherein theranking the first description suggestion is based on the selection bythe first client device.
 17. The method of claim 11, wherein thereceiving the data object from the first client device includes:receiving a reward value from the first client device, the reward valueoffered for an identification of the unidentified item of the dataobject based on the description suggestion; and wherein the methodfurther comprises: receiving a selection of the description suggestionfrom the first client device; identifying the unidentified item based onthe selection of the description suggestion; and awarding the rewardvalue to the second client device in response to the identifying theunidentified item.
 18. The method of claim 17, wherein the receiving theselection of the description suggestion includes: receiving a searchrequest from the first client device, wherein the search requestsincludes the description suggestion.
 19. A non-transitorymachine-readable storage medium comprising instructions that, whenexecuted by one or more processors of a machine, cause the machine toperform operations comprising: receiving, from a first client device, aquery that includes a request for an identification of an unidentifieditem, the request including a data object that comprises a text string;assigning the data object to an item category within a data repositorybased on the text string of the data object; receiving, from a secondclient device, an identification of the item category; causing displayof a representation of the request within a graphical user interface(GUI) at the second client device responsive to the identification ofthe item category, the GUI comprising a display of a set of graphicalelements representative of requests associated with the item categorywithin the data repository; receiving, from the second client device, aninput that selects the representation of the request, the inputincluding a description suggestion to be associated with the data objectwithin the data repository; and training an artificial intelligence (AI)module to identify the unidentified item based on the descriptionsuggestion associated with the data object within the data repository.20. The non-transitory machine-readable storage medium of claim 19,wherein the data object further comprises image data.